Dynamic DeepHit (DDH) [1]} is an extension of DeepHit, capable of processing longitudinal measurements. In this model, the encoder is replaced with RNN layers followed by an attention mechanism. The RNN block can learn the underlying relationship between longitudinal measurements and provide finer predictions compared to the MLP block in DeepHit. The case-specific blocks of DeepHit and DDH are the same, which means that DDH suffers from predicting noisy PDFs and overfitting.
